Underwater compressed air energy storage was developed from its terrestrial counterpart. It has also evolved to underwater compressed natural gas and hydrogen energy storage in recent years. UWCGES is a promising energy storage technology for the marine environment and subsequently of recent significant interest attention. However, it is still immature. In this study, the latest progress in both academic and industrial fields is summarized. Additionally, challenges facing this emerging technology are analyzed. The pros and cons of UWCGES are provided and are differentiated from the terrestrial variant. Technical, economic, environmental, and policy challenges are examined. In particular, the critical issues for developing artificial large and ultra-large underwater gas storage accumulators and effective underwater gas transportation are comprehensively analyzed. Finally, the demand for marine energy storage technology is briefly summarized, and the potential application scenarios and application modes of underwater compressed gas energy storage technology are prospected. This study aims to highlight the current state of the UWCGES sector and provide some guidance and reference for theoretical research and industrial development.

The low investment cost is one of the core competitiveness advantages of pneumatic power systems. With increasingly pressing intelligent manufacturing, it is meaningful to investigate the feasibility of implementing fault diagnoses of pneumatic systems with a minimal number of low-cost sensors. In this study, a typical pneumatic circuit with two parallel-installed cylinders is taken as an example. The pressure, flow rate, and exergy data collected from upstream sensors are used for diagnosing the leakage faults in two downstream cylinders with the help of different machine learning methods. The features of data are extracted with stacked auto-encoders. Gaussian process classifier, support vector machine, and k-nearest neighbor are used for classifying faults. The results show that it is feasible to detect and diagnose downstream multi-faults with one or two upstream sensors. In terms of the working conditions presented in this study, the average accuracy of diagnosis with exergy data is the highest, followed by flow-rate data and pressure data. The support vector machine performs the best among the three machine learning methods.

Underwater compressed gas energy storage (UW-CGES) holds significant promise as a nascent and viable energy storage solution for a diverse range of coastal and offshore facilities. However, liquid accumulation in underwater gas pipelines poses a significant challenge, as it can lead to pipeline blockages and energy transmission interruptions and adversely impact pipeline operation. In this paper, experimental and Oil and Gas Assays (OLGA) simulation studies have been conducted on the formation process of slug flow in pipelines. Firstly, experiments are conducted to capture high-speed camera images of slug flow under various liquid accumulation volumes and inclination angles. Subsequently, an OLGA model is developed to verify the experimentally observed flow regime, pressure, and slugging speed. Therefore, the flow regime verification results exhibit substantial consistency, and pressure variations display uniform trends, with an average slugging velocity error of 6.42%. The results indicate that the formation of slug flow involves three distinct stages: slug flow growth, ejection, and backflow. By analyzing slug flow, it can gain insights into the relationship between pressure and slug flow formation, exposing the sensitivity of this phenomenon to pressure fluctuations. These results further enhance recognition of the operational status of UW-CGES pipelines and provide support for safe operation.

Pneumatic systems are widely used in industrial manufacturing sectors. However, the energy efficiency of pneumatic systems is generally much lower than their hydraulic and electric counterparts. It is necessary to explore more elaborate theories and methods for achieving better energy performance in pneumatic systems. In this study, for investigating the interaction effects between pneumatic components and the accessible improvement potential of energy efficiency in a pre-existing pneumatic system, the advanced exergy analysis is conducted with a better understanding of exergy destruction. The conventional exergy analysis is also carried out for comparison. The results show that an exergy efficiency of 17.3% could be achieved under the real condition in the case of the investigated pneumatic system. However, under unavoidable conditions, the theoretical maximum exergy efficiency could reach 70.5%. This means there is a significant potential for improving the energy performance of the investigated system. Furthermore, both conventional and advanced exergy analyses indicate that the pneumatic cylinder has the greatest potential for improvement. The advanced exergy analysis reveals the complex and variable interactions between pneumatic components. It highlights that the exergy destruction of some components is caused by other components in the system, and thus, improving energy efficiency at the system level rather than at the component level is of great significance. Besides, a priority order of all pneumatic components is determined, thereby guiding the improvement of the energy efficiency of the pneumatic system.

Underwater flexible compressed air energy storage (UWFCAES) systems play a crucial role in offshore energy development and the mooring line is a core component ensuring the stability and safety of the system. However, long-term service in harsh marine environments makes the mooring line prone to fatigue damage, which poses a potential threat to the overall operational safety. To ensure the safe operation of the mooring system for UWFCAES systems, this study focuses on the mooring line and conducts systematic research on its dynamic tension response and time-variant fatigue reliability. The tension time-history simulation by LS-DYNA is performed and the T-N curve is adopted to estimate the fatigue life. The time-variant reliability model is established based on the load-strength interference theory to analyze the variations of residual strength, failure probability, reliability, and reliability index over a 30-year service period. The results show that the estimated fatigue life of the mooring line reaches 8.025×10¹¹ cycles. During the 30-year service period, the residual strength degradation is negligible, the failure probability is maintained at the 10⁻¹¹ order of magnitude, and the reliability index is 6.3902, indicating sufficient safety margin. The established analysis framework provides theoretical and technical support for the optimal design and operation and maintenance of mooring systems for UWFCAES systems.